基于GEE的滇池流域土地覆盖变化及建设用地扩张驱动力分析

Analysis of Driving Forces for Land Cover Change and Urban Land Expansion in Dianchi Lake Basin Based on GEE

  • 摘要: 以滇池流域为研究区,基于Google Earth Engine云平台,选取Landsat遥感影像,采用支持向量机分类方法,提取林地、草地、农地、水体、建设用地、其他未利用地共6类土地覆盖类型,以行政区划为单元来分析1988—2018年土地覆盖变化的特点;通过熵值法与灰色关联法来分析不同时间段内影响滇池流域建设用地扩张的驱动力。结果表明:1988—2018年,滇池流域内林地、建设用地、其他未利用地面积持续增长,而草地、农地、水体面积持续减少。1988—2018年,建设用地为最大的转入类型,林地、草地的转出面积较多。1998—2018年,滇池流域内土地覆盖变化最为剧烈的为官渡区、五华区与呈贡区,其余区县的土地覆盖变化过程较为缓和。1988—2018年,人均道路铺装面积、社会销售品销售总额与客运总量这3项因子与建设用地的关联度较高;1988—2008年间,经济驱动因素与人口驱动因素的贡献比重较高;2008—2018年,教育、卫生条件驱动因素与基础建设驱动因素的贡献比重较高。

     

    Abstract: This study takes Dianchi Lake Basin as the study area. Landsat remote sensing images are selected based on Google Earth Engine cloud platform. Support vector machine classification method is used to extract forest, grass, farmland, water, construction land, other unused land. Administrative divisions were used as a unit to analyse the characteristics of land cover changes in 1988–2018. The entropy method and grey correlation method are used to analyze the driving forces affecting the expansion of construction land in Dianchi Lake Basin in different time periods. The results showed that the area of forest land, construction land and other unused land in Dianchi Lake Basin increased from 1988 to 2018, while the area of grassland, farmland and water body decreased. From 1988 to 2018, construction land was the largest transfer type, and the lose area of forest and grassland was large. From 1998 to 2018, the most drastic changes in land cover in Dianchi Lake basin were Guandu District, Wuhua District and Chenggong District, while the process of land cover change in other districts was relatively slow. From 1988 to 2018, the 3 factors of per capita road paving area, total sales of social products, and total passenger traffic were highly correlated with construction land; from 1988 to 2008, the proportion of economic driving factors and population driving factors high; from 2008 to 2018, the driving factors of education and health conditions and the driving factors of infrastructure construction contributed a relatively high proportion.

     

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